Digital color imaging technology has been widely used in various multimedia peripheral devices and image capturing apparatuses, for example, digital still camera, digital video recorder and so on. In some situations the color of an object itself will be different in accordance with the change of the color of incident light, however, human eye will adapt to the colors of incident light source quickly and automatically. Unfortunately, image capturing apparatus does not provide with the function of achieving natural color. To correct the color derivation resulting from source light and obtain more natural looking colors, a crucial element is typically used in digital color image processing applications, which is known as white balance technique.
The purpose of white balance correction is to allow the image capturing apparatus to function as human eyes, which automatically adjust internal color balance by calculating an average of different illuminations, such that the hue and tinge of white color can be actually displayed under all conditions. In other words, automatic white balance technique generally adjusts the intensities of three original colors—red, green and blue within the entire image according to computation result of the image properties in the present digital image frame, and thereby correct the color deviation resulting from external source light.
The conventional automatic white balance correction methodology normally uses white points to perform color image correction. An example of the conventional art is described in U.S. Pat. No. 6,069,972, which is incorporated herein for reference. The automatic white balance correction methodology suggested in this conventional art reference accomplishes a global search to the captured color image. After all the white points defining a color image have been searched out, an average of these white point component values is evaluated. Subsequently, the evaluated average is used to compute the color correction coefficients of the white point component values with respect to each pixel component values. Eventually, the computed color correction coefficients are used to perform color correction to each pixel components of the color image, and further automatically adjusting to obtain the most appropriate white balance.
However, in the above steps of white point searching, white point color correction coefficient computation and color correction, each step requires to search the entire color image, i.e. the brightest white point must be searched out, which leads to the consumption of longer time. In addition, the above-described white balance methodology requires to identify and record the white points in the entire image so as to compute the white point color correction coefficient. This will explicitly indicate that a larger memory space is needed to store considerable computational data, and on the other hand, this will implicitly indicate that the work loading of hardware integrated circuit will increase due to enormous computation quantity.